Lamp array optimization data analysis system and method for plant illumination

Abstract

The invention discloses a lamp array optimization data analysis system and method for plant illumination, and relates to the technical field of lamp array optimization, and the analysis method comprises the following steps: pre-deploying a monitoring device in a plant growth area, and collecting illumination data in the plant growth area; dividing the plant growth area into a plurality of illumination sub-areas; evaluating the light intensity value of each illumination sub-region; difference comparison is carried out on the light intensity values of the different illumination sub-areas, and a plurality of deviation areas are identified; performing deviation characteristic capture on the illumination data in any deviation area; analyzing the expected adjustment state of each deviation feature, and identifying the optimization demand of the deviation region; generating a corresponding optimization demand instruction for any deviation area, and extracting a target lamp array meeting the optimization demand; formulating a lamp array arrangement scheme for the target lamp array of each deviation area for adjustment; and early discovery and accurate intervention of the illumination problem are realized.

Description

Technical Field

[0001] This invention relates to the field of light array optimization technology, specifically to a light array optimization data analysis system and method for plant lighting. Background Technology

[0002] With the rapid development of facility agriculture and plant factories, the role of artificial light sources in the plant growth process is becoming increasingly prominent. Especially in scenarios where light conditions are limited or where precise control of the light environment is required, light array lighting systems have become key equipment to ensure efficient photosynthesis in plants. Currently, plant lighting mostly adopts uniform lamp distribution or simple arrangement based on experience. It lacks the ability to dynamically adapt to the actual light field distribution and the light requirements of plants, and lacks a refined identification and targeted optimization mechanism for regional light intensity differences. It is difficult to achieve precise control of light distribution, resulting in problems such as low light energy utilization efficiency, insufficient or excessive local light intensity, which limits the further improvement of light efficiency and plant growth quality. Summary of the Invention

[0003] The purpose of this invention is to provide a data analysis system and method for optimizing light arrays for plant lighting, in order to solve the problems raised in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing light array data for plant lighting, the method comprising the following steps: Step S100: Pre-deploy monitoring equipment in the plant growth area to collect lighting data in the plant growth area; divide the plant growth area into several lighting sub-areas according to the plant distribution; Step S200: Evaluate the light intensity value of each lighting sub-region based on the collected lighting data; compare the differences in light intensity values ​​between different lighting sub-regions to identify several deviation regions that cause light intensity deviation; Step S300: Capture the deviation features of the lighting data in any deviation area; analyze the expected adjustment state of each deviation feature and identify the optimization needs of the deviation area; Step S400: Generate corresponding optimization requirement instructions for any deviation area, extract the target light array that meets the optimization requirements, and formulate and adjust the light array layout scheme for the target light array in each deviation area.

[0005] Furthermore, step S100 includes the following steps: Step S101: Establish a two-dimensional Cartesian coordinate system in the plant growth area, determine the position of the light array installed above the plant growth area, and obtain the position coordinates of each light bead in the light array; capture the shadow range of each plant under the illumination of the light array in the plant growth area, and obtain the area range of each plant in the coordinate system; arbitrarily select the area range of a plant, summarize all the light beads whose position coordinates belong to the area range, and set it as the set of light beads of the selected plant; Step S102: Arrange the LEDs in the light array according to the preset arrangement rules and number each LED; pre-deploy several monitoring devices, pre-set several monitoring indicators for each monitoring device, collect the lighting data of each LED in the light array under each monitoring indicator at each unit time point, and summarize the lighting data collected by each monitoring device at each unit cycle to obtain the lighting data set of each LED in any unit cycle; the preset monitoring indicators for the monitoring devices include light intensity, illumination time, illumination angle, color temperature, etc. Step S103: Obtain the area range of each plant in the plant growth area. Randomly select two plants. If there is a common area between the area ranges of the two plants or no area range of other plants, and the two plants are of the same species, then set the two selected plants as plants in the same area. Summarize all plants in the same area and perform a union operation on the area ranges of the plants in the same area to obtain a whole area of ​​plants in the same area and set it as a lighting sub-area of ​​the plant growth area. Summarize the LED sets of each plant in any lighting sub-area to obtain the sub-light array of the lighting sub-area.

[0006] Furthermore, step S200 includes the following steps: Step S201: Arbitrarily select a lighting sub-region to obtain a sub-lamp array for the selected lighting sub-region, and arbitrarily select a unit period to extract the lighting data set of each lamp in the sub-lamp array during the selected unit period; a lighting database is pre-constructed, which stores lighting indicators of several dimensions, and each lighting indicator is matched with a corresponding light intensity evaluation rule. The monitoring indicators in the lighting data set are compared with the lighting indicators, and the monitoring indicators that are the same as the lighting indicators are set as target indicators to obtain the light intensity evaluation rules for each target indicator; the lighting indicators stored in the lighting database include illuminance, illuminance uniformity, illuminance directionality, etc., and the corresponding light intensity evaluation rules, such as illuminance, can be directly used to obtain the light intensity value, and illuminance uniformity can be obtained by the ratio of minimum light intensity to average light intensity. Step S202: Randomly select the i-th LED from the sub-lamp array, and simultaneously extract the j-th unit time point from the selected unit period to obtain the illumination data of each target indicator for the i-th LED at the j-th unit time point. Randomly select the illumination data of the k-th target indicator, and obtain the light intensity value G of the k-th target indicator according to the light intensity evaluation rule. k (i,j), the light intensity values ​​of all target indicators are summed to obtain the light intensity value G(i,j) of the i-th LED at the j-th unit time point, according to the formula: ; Where a is the number of LEDs in the sub-lamp array, and b is the number of time points per unit period; the comprehensive light intensity value L of the selected lighting sub-region under the selected unit period is calculated; Step S203: Set the plant species in the selected illumination sub-region as the target species, extract the remaining illumination sub-regions with the same plant species as the target species, and set them as target sub-regions; arbitrarily select a target sub-region, acquire the light intensity value of the selected target sub-region in each unit cycle, and calculate the average light intensity value of the selected target sub-region; set the average light intensity value of the p-th target sub-region as L. (ave,p) According to the formula: ; Where M is the number of target sub-regions; the mean μ and standard deviation σ of the average light intensity values ​​among all target sub-regions are calculated; Step S204: Preset a light intensity deviation amplitude θ, and obtain the expected deviation difference value ΔL. ex =θ×σ, if |L (ave,p) -μ|≥ΔL ex Then, the p-th target sub-region is set as the deviation region; the lighting sub-regions corresponding to various types of plants are identified, and several deviation regions of the plant growth area are generated.

[0007] Furthermore, step S300 includes the following steps: Step S301: Randomly select a deviation area, extract the sub-lamp array of the selected deviation area, and obtain the comprehensive light intensity value L of the selected deviation area under a certain unit period. ’ The expected deviation difference of the selected deviation area is set to ΔL. ’ The mean of the relevant illumination sub-region is μ ’ If |L ’ -μ ’ |≥ΔL ’ Then, the aforementioned unit period is set as the deviation period, if |L ’ -μ ’ |<ΔL ’Then, the aforementioned unit period will be set as the normal period; Step S302: Extract any normal period from the selected deviation area to obtain the lighting data of each monitoring indicator at each unit time point. Select any monitoring indicator to obtain the lighting data range corresponding to the selected monitoring indicator in the normal period. Step S303: Extract a deviation period from the selected deviation area. Randomly select a unit time point from the deviation period and acquire the lighting data of the selected monitoring indicator at that unit time point. If the lighting data of the selected monitoring indicator is not within the corresponding lighting data range at the selected unit time point, then set the selected monitoring indicator as an abnormal indicator of the deviation period. Count N the number of abnormal indicators in the deviation period. exp Set the number of monitoring indicators to N. total The abnormal proportion η=N of a certain deviation period is calculated. exp / N total Preset an abnormality percentage threshold η th If η≥η th Then, the aforementioned deviation period is set as the target deviation period; Step S304: Randomly select a target deviation period from the selected deviation area, and randomly select a lamp from the selected sub-lamp array. Extract the lighting data of each monitoring indicator of the selected lamp in the selected target deviation period. If the lighting data of a certain monitoring indicator is not in the corresponding lighting data range, then set the certain monitoring indicator as a deviation feature to obtain several deviation features of the selected deviation area. Step S305: Set the lighting data of a certain monitoring indicator as D1, and the corresponding lighting data range is (D ’ D ’’ The abnormal difference value of a certain monitoring indicator is obtained as ΔD = Max(D1 - D). ’’ D ’ -D1); Randomly select a deviation feature from the selected deviation region, and extract the abnormal difference value of the selected deviation feature in each target deviation period to obtain the maximum abnormal difference value ΔD of the selected deviation feature. max And according to the corresponding light intensity evaluation rules, the maximum light intensity difference ΔG corresponding to the maximum abnormal difference is obtained. max The maximum light intensity difference of each deviation feature is accumulated to obtain the maximum demand adjustment value ΔG for the selected deviation area. need If |L ’ -μ ’ -ΔG need |≥ΔL ’ Then, set the first optimization requirement marker for the selected deviation area, |L ’-μ ’ -ΔG need |<ΔL ’ Then, a second optimization requirement flag is set for the selected deviation area; the first optimization requirement flag indicates that the current deviation area cannot meet the requirements through its own adjustment and needs to retrieve a new light array from other areas, while the second optimization requirement flag indicates that the deviation area can meet the requirements through the angle or brightness adjustment of its own light array.

[0008] Furthermore, step S400 includes the following steps: Step S401: Randomly select a deviation region and obtain the optimization requirement mark of the selected deviation region; if the selected deviation region is the first optimization requirement mark, then extract the adjacent sub-regions adjacent to the selected deviation region, and extract several lamp beads from the sub-lamp arrays in the adjacent sub-regions. The light intensity values ​​of the several lamp beads are accumulated to obtain the total light intensity value G1, and the maximum requirement adjustment value ΔG of the selected deviation region is set. need If |G need +L ’ -μ ’ -ΔG need |<ΔL ’ If the selected area is the second optimization requirement marker, then the selected sub-array of the selected area is set as the target array. Step S402: Generate an optimization instruction for the target light array, wherein the optimization instruction includes adjusting at least one parameter, either the brightness or the illumination direction of the lamp beads in the target light array; according to the optimization instruction, re-collect the illumination data of each lamp bead in the target light array to obtain the expected light intensity value of the target light array; if the difference between the expected light intensity value and the average value of the illumination sub-region of the same plant species is less than the expected deviation difference, then confirm the light array layout scheme and perform the adjustment; otherwise, reselect the target light array or adjust the parameters until the conditions are met.

[0009] To better implement the above methods, a data analysis system for optimizing light arrays is also proposed. The analysis system includes a regional lighting acquisition module, a light intensity difference analysis module, a deviation demand analysis module, and a light array layout optimization module. The regional lighting analysis module is used to pre-deploy monitoring equipment in the plant growth area to collect lighting data in the plant growth area; and to divide the plant growth area into several lighting sub-areas according to the plant distribution. The light intensity difference analysis module is used to evaluate the light intensity value of each lighting sub-region based on the collected lighting data; compare the differences in light intensity values ​​between different lighting sub-regions; and identify several deviation regions that cause light intensity deviation. The deviation requirement analysis module is used to capture deviation features of lighting data in any deviation area; analyze the expected adjustment state of each deviation feature, and identify the optimization requirements of the deviation area. The light array layout optimization module is used to generate corresponding optimization requirement instructions for any deviation area, extract the target light array that meets the optimization requirements, and formulate and adjust the light array layout scheme for the target light array in each deviation area.

[0010] Furthermore, the area lighting analysis module includes a lighting data acquisition unit and a lighting area division unit; The lighting data acquisition unit is used to pre-deploy monitoring equipment in the plant growth area to collect lighting data in the plant growth area; the lighting area division unit is used to divide the plant growth area into several lighting sub-areas according to the plant distribution.

[0011] Furthermore, the light intensity difference analysis module includes a regional light intensity analysis unit and a deviation region identification unit; The regional light intensity analysis unit is used to evaluate the light intensity value of each lighting sub-region based on the collected lighting data; the deviation region identification unit is used to compare the differences in light intensity values ​​between different lighting sub-regions and identify several deviation regions that cause light intensity deviation.

[0012] Furthermore, the deviation requirement analysis module includes a deviation feature capture unit and an optimization requirement identification unit; The deviation feature capture unit is used to capture the deviation features of lighting data in any deviation area; the optimization requirement identification unit is used to analyze the expected adjustment state of each deviation feature and identify the optimization requirements of the deviation area.

[0013] Furthermore, the light array layout optimization module includes a target light array extraction unit and a light array layout adjustment unit; The target light array extraction unit is used to generate corresponding optimization requirement instructions for any deviation area and extract the target light array that meets the optimization requirements; the light array layout adjustment unit is used to adjust the light array layout scheme of the target light array in each deviation area.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention achieves precise perception and dynamic monitoring of light conditions in plant growth areas by finely dividing the lighting sub-regions and evaluating light intensity based on plant species and distribution characteristics. It effectively solves the problem of uneven local light intensity caused by traditional uniform lighting methods and significantly improves the pertinence and scientific nature of light environment regulation. 2. This invention can automatically detect and locate areas of abnormal light intensity, and combine multi-dimensional lighting indicators for in-depth diagnosis, thereby accurately identifying optimization needs. It overcomes the limitations of traditional methods that rely on human experience and have a slow response, and realizes early detection and precise intervention of lighting problems. 3. By establishing an optimization demand labeling and target lamp array extraction mechanism, the system can intelligently formulate adjustment strategies according to different deviations, support multi-parameter linkage control of lamp brightness, illumination direction, etc., and has strong adaptive optimization capabilities, effectively improving light energy utilization efficiency. Attached Figure Description

[0015] Figure 1 A schematic diagram illustrating the steps of data analysis for optimizing light arrays for plant lighting; Figure 2 A schematic diagram of the structure of a data analysis system for optimizing light arrays for plant lighting. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example: Figures 1 to 2 As shown, this invention provides a data analysis method for optimizing light arrays for plant lighting. The analysis method includes the following steps: Step S100: Pre-deploy monitoring equipment in the plant growth area to collect lighting data in the plant growth area; divide the plant growth area into several lighting sub-areas according to the plant distribution; Step S100 includes the following steps: Step S101: Establish a two-dimensional Cartesian coordinate system in the plant growth area, determine the position of the light array installed above the plant growth area, and obtain the position coordinates of each light bead in the light array; capture the shadow range of each plant under the illumination of the light array in the plant growth area, and obtain the area range of each plant in the coordinate system; arbitrarily select the area range of a plant, summarize all the light beads whose position coordinates belong to the area range, and set it as the set of light beads of the selected plant; Example 1: A two-dimensional Cartesian coordinate system is established in the plant growth area, with the origin set at the lower left corner. The position coordinates of each LED bead are determined, for example: LED bead 1 is at (0.5, 0.5), LED bead 2 is at (0.5, 1.5), ..., LED bead 100 is at (9.5, 9.5). The area of ​​plant shadow is captured by a camera, and the area of ​​plant A is a rectangle from (1,1) to (2,2), and the area of ​​plant B is from (2,1) to (3,2). For plant A, the LED beads within the rectangle from (1,1) to (2,2) are grouped into the LED bead set of plant A. Step S102: Arrange the LEDs in the light array according to the preset arrangement rules and number each LED; pre-deploy several monitoring devices, pre-set several monitoring indicators for each monitoring device, collect the lighting data of each LED in the light array under each monitoring indicator at each unit time point, and summarize the lighting data collected by each monitoring device at each unit cycle to obtain the lighting data set of each LED in any unit cycle. Step S103: Obtain the area range of each plant in the plant growth area. Randomly select two plants. If there is a common area between the area ranges of the two plants or no area range of other plants, and the two plants are of the same species, then set the two selected plants as plants in the same area. Summarize all plants in the same area and perform a union operation on the area ranges of the plants in the same area to obtain a whole area of ​​plants in the same area and set it as a lighting sub-area of ​​the plant growth area. Summarize the LED sets of each plant in any lighting sub-area to obtain the sub-light array of the lighting sub-area.

[0018] Step S200: Evaluate the light intensity value of each lighting sub-region based on the collected lighting data; compare the differences in light intensity values ​​between different lighting sub-regions to identify several deviation regions that cause light intensity deviation; Step S200 includes the following steps: Step S201: Arbitrarily select a lighting sub-region to obtain a sub-lamp array of the selected lighting sub-region, and arbitrarily select a unit period to extract the lighting data set of each lamp in the sub-lamp array during the selected unit period; a lighting database is pre-constructed, which stores lighting indicators of several dimensions, and each lighting indicator is matched with a corresponding light intensity evaluation rule. The monitoring indicators in the lighting data set are compared with the lighting indicators, and the monitoring indicators that are the same as the lighting indicators are set as target indicators to obtain the light intensity evaluation rules of each target indicator. Step S202: Randomly select the i-th LED from the sub-lamp array, and simultaneously extract the j-th unit time point from the selected unit period to obtain the illumination data of each target indicator for the i-th LED at the j-th unit time point. Randomly select the illumination data of the k-th target indicator, and obtain the light intensity value G of the k-th target indicator according to the light intensity evaluation rule. k (i,j), the light intensity values ​​of all target indicators are summed to obtain the light intensity value G(i,j) of the i-th LED at the j-th unit time point, according to the formula: ; Where a is the number of LEDs in the sub-lamp array, and b is the number of time points per unit period; the comprehensive light intensity value L of the selected lighting sub-region under the selected unit period is calculated; Example 2: Select lighting sub-region 1, and select the first day as the unit period, resulting in 6 LEDs in the sub-light array and 24 unit time points; the lighting database stores lighting indicators including light intensity, and the light intensity evaluation rule is to directly use the light intensity value. After comparing the monitoring indicators with the lighting indicators, the target indicator is light intensity; set the average light intensity values ​​of the 6 LEDs in the 24 unit time points to be 500, 400, 600, 480, 510, and 500 respectively, then sum the 6 LEDs to obtain the comprehensive light intensity value L=2990; Step S203: Set the plant species in the selected illumination sub-region as the target species, extract the remaining illumination sub-regions with the same plant species as the target species, and set them as target sub-regions; arbitrarily select a target sub-region, acquire the light intensity value of the selected target sub-region in each unit cycle, and calculate the average light intensity value of the selected target sub-region; set the average light intensity value of the p-th target sub-region as L. (ave,p) According to the formula: ; Where M is the number of target sub-regions; the mean μ and standard deviation σ of the average light intensity values ​​among all target sub-regions are calculated; Step S204: Preset a light intensity deviation amplitude θ, and obtain the expected deviation difference value ΔL. ex =θ×σ, if |L (ave,p) -μ|≥ΔL ex Then, the p-th target sub-region is set as the deviation region; the lighting sub-regions corresponding to various types of plants are identified, and several deviation regions of the plant growth area are generated; Example 3: The plant species in illumination sub-region 1 is lettuce. Other lettuce illumination sub-regions, such as sub-region 2 and sub-region 3, are extracted as target sub-regions. Their average light intensity values ​​are L... (ave,1) =480, L (ave,2) =520, L (ave,3)=500, resulting in a mean μ=500 and a standard deviation σ=16.33; preset light intensity deviation amplitude θ=1.5, expected deviation difference ΔL ex =θ×σ=1.5×16.33=24.5; For each target sub-region, sub-region 1 satisfies |480-500|=20<24.5, so it is a non-deviation region; sub-region 2 satisfies |520-500|=20<24.5, so it is a non-deviation region; sub-region 3 satisfies |500-500|=0<24.5, so it is a non-deviation region; Assuming that the average light intensity of sub-region 4 is 450, then |450-500|=50>24.5, so it is identified as a deviation region.

[0019] Step S300: Capture the deviation features of the lighting data in any deviation area; analyze the expected adjustment state of each deviation feature and identify the optimization needs of the deviation area; Step S300 includes the following steps: Step S301: Randomly select a deviation area, extract the sub-lamp array of the selected deviation area, and obtain the comprehensive light intensity value L of the selected deviation area under a certain unit period. ’ The expected deviation difference of the selected deviation area is set to ΔL. ’ The mean of the relevant illumination sub-region is μ ’ If |L ’ -μ ’ |≥ΔL ’ Then, the aforementioned unit period is set as the deviation period, if |L ’ -μ ’ |<ΔL ’ Then, the aforementioned unit period will be set as the normal period; Step S302: Extract any normal period from the selected deviation area to obtain the lighting data of each monitoring indicator at each unit time point. Select any monitoring indicator to obtain the lighting data range corresponding to the selected monitoring indicator in the normal period. Step S303: Extract a deviation period from the selected deviation area. Randomly select a unit time point from the deviation period and acquire the lighting data of the selected monitoring indicator at that unit time point. If the lighting data of the selected monitoring indicator is not within the corresponding lighting data range at the selected unit time point, then set the selected monitoring indicator as an abnormal indicator of the deviation period. Count N the number of abnormal indicators in the deviation period. exp Set the number of monitoring indicators to N. total The abnormal proportion η=N of a certain deviation period is calculated. exp / N total Preset an abnormality percentage threshold η thIf η≥η th Then, the aforementioned deviation period is set as the target deviation period; Step S304: Randomly select a target deviation period from the selected deviation area, and randomly select a lamp from the selected sub-lamp array. Extract the lighting data of each monitoring indicator of the selected lamp in the selected target deviation period. If the lighting data of a certain monitoring indicator is not in the corresponding lighting data range, then set the certain monitoring indicator as a deviation feature to obtain several deviation features of the selected deviation area. Step S305: Set the lighting data of a certain monitoring indicator as D1, and the corresponding lighting data range is (D ’ D ’’ The abnormal difference value of a certain monitoring indicator is obtained as ΔD = Max(D1 - D). ’’ D ’ -D1); Randomly select a deviation feature from the selected deviation region, and extract the abnormal difference value of the selected deviation feature in each target deviation period to obtain the maximum abnormal difference value ΔD of the selected deviation feature. max And according to the corresponding light intensity evaluation rules, the maximum light intensity difference ΔG corresponding to the maximum abnormal difference is obtained. max The maximum light intensity difference of each deviation feature is accumulated to obtain the maximum demand adjustment value ΔG for the selected deviation area. need If |L ’ -μ ’ -ΔG need |≥ΔL ’ Then, set the first optimization requirement marker for the selected deviation area, |L ’ -μ ’ -ΔG need |<ΔL ’ Then, a second optimization requirement marker is set for the selected deviation area.

[0020] Step S400: Generate corresponding optimization requirement instructions for any deviation area, extract the target light array that meets the optimization requirements; formulate and adjust the light array layout scheme for the target light array in each deviation area. Step S400 includes the following steps: Step S401: Randomly select a deviation region and obtain the optimization requirement mark of the selected deviation region; if the selected deviation region is the first optimization requirement mark, then extract the adjacent sub-regions adjacent to the selected deviation region, and extract several lamp beads from the sub-lamp arrays in the adjacent sub-regions. The light intensity values ​​of the several lamp beads are accumulated to obtain the total light intensity value G1, and the maximum requirement adjustment value ΔG of the selected deviation region is set. need If |G need +L ’ -μ’ -ΔG need |<ΔL ’ If the selected area is the second optimization requirement marker, then the selected sub-array of the selected area is set as the target array. Step S402: Generate an optimization instruction for the target light array, wherein the optimization instruction includes adjusting at least one parameter, either the brightness or the illumination direction of the lamp beads in the target light array; according to the optimization instruction, re-collect the illumination data of each lamp bead in the target light array to obtain the expected light intensity value of the target light array; if the difference between the expected light intensity value and the average value of the illumination sub-region of the same plant species is less than the expected deviation difference, then confirm the light array layout scheme and perform the adjustment; otherwise, reselect the target light array or adjust the parameters until the conditions are met.

[0021] A data analysis system for optimizing a light array includes a regional lighting acquisition module, a light intensity difference analysis module, a deviation demand analysis module, and a light array layout optimization module. The regional lighting analysis module is used to pre-deploy monitoring equipment in the plant growth area to collect lighting data in the plant growth area; and to divide the plant growth area into several lighting sub-areas according to the plant distribution. The light intensity difference analysis module is used to evaluate the light intensity value of each lighting sub-region based on the collected lighting data; compare the differences in light intensity values ​​between different lighting sub-regions; and identify several deviation regions that cause light intensity deviation. The deviation requirement analysis module is used to capture deviation features of lighting data in any deviation area; analyze the expected adjustment state of each deviation feature, and identify the optimization requirements of the deviation area. The light array layout optimization module is used to generate corresponding optimization requirement instructions for any deviation area, extract the target light array that meets the optimization requirements, and formulate and adjust the light array layout scheme for the target light array in each deviation area.

[0022] The regional lighting analysis module includes a lighting data acquisition unit and a lighting area division unit; The lighting data acquisition unit is used to pre-deploy monitoring equipment in the plant growth area to collect lighting data in the plant growth area; the lighting area division unit is used to divide the plant growth area into several lighting sub-areas according to the plant distribution.

[0023] The light intensity difference analysis module includes a regional light intensity analysis unit and a deviation region identification unit. The regional light intensity analysis unit is used to evaluate the light intensity value of each lighting sub-region based on the collected lighting data; the deviation region identification unit is used to compare the differences in light intensity values ​​between different lighting sub-regions and identify several deviation regions that cause light intensity deviation.

[0024] The deviation requirement analysis module includes a deviation feature capture unit and an optimization requirement identification unit. The deviation feature capture unit is used to capture the deviation features of lighting data in any deviation area; the optimization requirement identification unit is used to analyze the expected adjustment state of each deviation feature and identify the optimization requirements of the deviation area.

[0025] The light array layout optimization module includes a target light array extraction unit and a light array layout adjustment unit. The target light array extraction unit is used to generate corresponding optimization requirement instructions for any deviation area and extract the target light array that meets the optimization requirements; the light array layout adjustment unit is used to adjust the light array layout scheme of the target light array in each deviation area.

[0026] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method of data analysis for optimization of light arrays for plant illumination, characterized by: The analysis method comprises the following steps: Step S100: Pre-deploying monitoring devices in the plant growth area to collect lighting data in the plant growth area; dividing the plant growth area into a plurality of lighting sub-areas according to the plant distribution; Step S200: Evaluating the light intensity values of each lighting sub-area based on the collected lighting data; comparing the light intensity values between different lighting sub-areas to identify a plurality of deviation areas that deviate from the light intensity values; Step S300: Capturing the deviation characteristics of the lighting data in any deviation area; analyzing the expected adjustment state of each deviation characteristic to identify the optimization requirements of the deviation area; Step S400: Generating corresponding optimization requirement instructions for any deviation area, extracting target lamp arrays that meet the optimization requirements, and adjusting the lamp array arrangement scheme for each target lamp array in each deviation area.

2. The method of claim 1, wherein: The step S100 comprises the following steps: Step S101: Establishing a two-dimensional plane rectangular coordinate system in the plant growth area, determining the positions of the lamp arrays installed above the plant growth area to obtain the position coordinates of each lamp bead in the lamp array, capturing the shadow range of each plant in the plant growth area under the irradiation of the lamp array to obtain the area range of each plant in the coordinate system, and arbitrarily selecting the area range of a plant, collecting all lamp beads with position coordinates belonging to the area range, and setting them as the lamp bead set of the selected plant; Step S102: Arranging the lamp beads in the lamp array according to a preset arrangement rule, and numbering each lamp bead; pre-arranging a plurality of monitoring devices, pre-setting a plurality of monitoring indexes for each monitoring device, collecting the lighting data of each lamp bead in the lamp array under each monitoring index every unit time point, and collecting the lighting data collected by each monitoring device every unit period to obtain the lighting data set of each lamp bead in any unit period; Step S103: Obtaining the area range of each plant in the plant growth area, arbitrarily selecting two plants, and if the area ranges of the two plants have a common range or do not have the area ranges of the remaining plants, and the plant species of the two plants are the same, then the two selected plants are set as same-area plants; collecting all same-area plants and performing a set intersection operation on the area ranges of the same-area plants to obtain a whole area of the same-area plants and set it as a lighting sub-area of the plant growth area; and collecting the lamp bead sets of each plant in any lighting sub-area to obtain a sub-lamp array of the lighting sub-area.

3. The method of claim 2, wherein: The step S200 comprises the following steps: Step S201: randomly select one lighting sub-region to obtain a sub-lamp array of the selected lighting sub-region, and randomly select one unit period to extract lighting data sets of each lamp bead in the sub-lamp array in the selected unit period; a lighting database is preset to store lighting indicators of several dimensions, and each lighting indicator is matched with a corresponding light intensity evaluation rule; each monitoring indicator in the lighting data set is compared with the lighting indicator, and the monitoring indicator identical to the lighting indicator is set as a target indicator to obtain a light intensity evaluation rule of each target indicator; Step S202: Select the i th lamp bead from the sub-lamp array, and extract the j th unit time point from the selected unit period, to obtain the lighting data of each target index of the i th lamp bead at the j th unit time point, and randomly select the k th target index lighting data, and obtain the light intensity value of the k th target index according to the light intensity evaluation rule G k (i,j), and accumulate the light intensity values of all target indexes to obtain the light intensity value G(i,j) of the i th lamp bead at the j th unit time point, according to the formula: ； Wherein, a is the number of lamp beads in the sub-lamp array, and b is the number of unit time points in the unit period; the integrated light intensity value L of the selected lighting sub-region in the selected unit period is calculated; Step S203: set the plant species in the selected illumination sub-region as the target species, extract the remaining illumination sub-regions with the same target species, and set them as target sub-regions; randomly select one target sub-region, obtain the light intensity value in each unit period in the selected target sub-region, calculate the average value to obtain the average light intensity value of the selected target sub-region; set the average light intensity value of the pth target sub-region as L (ave,p) , according to the formula: ； Wherein, M is the number of target sub-regions; the mean value μ and the standard deviation σ of the average light intensity value between all target sub-regions are calculated; Step S204: A light intensity deviation amplitude θ is preset to obtain an expected deviation value ΔL ex = θ x σ, if |L (ave,p) - μ | ≥ ΔL ex The pth target sub-region is set as a deviation region; and the lighting sub-regions corresponding to various plants are identified to generate a plurality of deviation regions of the plant growth region.

4. The method of claim 3, wherein: The step S300 includes the following steps: Step S301: Arbitrarily select a deviation region, extract the sub-lamp array of the selected deviation region, and obtain the integrated light intensity value L of the selected deviation region in a certain unit period ’ , set the expected deviation difference value of the selected deviation region as ΔL ’ , and the mean value of the related illumination sub-region as μ ’ , if |L ’ -μ ’ |≥ΔL ’ , set the certain unit period as a deviation period, if |L ’ -μ ’ |<ΔL ’ , set the certain unit period as a normal period; Step S302: extract any one normal period in the selected deviation region to obtain lighting data of each monitoring indicator at each unit time point, and randomly select one monitoring indicator to obtain a lighting data range corresponding to the selected monitoring indicator in the normal period; Step S303: Extract a deviation period in the selected deviation region, randomly select a unit time point in the deviation period, acquire the lighting data of the selected monitoring index at the selected unit time point, and if the lighting data of the selected monitoring index at the selected unit time point is not within the corresponding lighting data range, set the selected monitoring index as an abnormal index of the deviation period; count the number of abnormal indexes in the deviation period as N exp , set the number of monitoring indexes as N total , calculate the abnormal proportion η = N exp / N total , preset an abnormal proportion threshold η th , and if η ≥ η th , set the deviation period as a target deviation period; Step S304: randomly select one target deviation period from the selected deviation region, and randomly select one lamp bead from the selected sub-lamp array to extract lighting data of each monitoring indicator of the selected lamp bead in the selected target deviation period; if the lighting data of a certain monitoring indicator is not in the corresponding lighting data range, the certain monitoring indicator is set as a deviation feature to obtain several deviation features of the selected deviation region; Step S305: set the lighting data of the certain monitoring index as D1, and the corresponding lighting data range as (D ’ , D ’’ ), obtain the abnormal difference value of the certain monitoring index as ΔD=Max(D1-D ’’ , D ’ -D1); select any deviation feature from the selected deviation region, extract the abnormal difference value of the selected deviation feature in each target deviation period, obtain the maximum abnormal difference value ΔD max of the selected deviation feature, and obtain the maximum light intensity difference value ΔG max corresponding to the maximum abnormal difference value according to the corresponding light intensity evaluation rule; accumulate the maximum light intensity difference value of each deviation feature to obtain the maximum demand adjustment value ΔG need of the selected deviation region; if |L ’ -μ ’ -ΔG need |≥ΔL ’ , set a first optimization demand mark for the selected deviation region; if |L ’ -μ ’ -ΔG need |<ΔL ’ , set a second optimization demand mark for the selected deviation region.

5. The method of claim 4, wherein: The step S400 includes the following steps: Step S401: arbitrarily select a deviation region, obtain the optimization demand mark of the selected deviation region; if the selected deviation region is the first optimization demand mark, extract an adjacent sub-region adjacent to the selected deviation region, and extract a plurality of lamp beads from the sub-lamp array in the adjacent sub-region, accumulate the light intensity values of the plurality of lamp beads to obtain a total light intensity value G1, and set the maximum demand adjustment value ΔG of the selected deviation region need , if |G need +L ’ -μ ’ -ΔG need |<ΔL ’ , set the plurality of lamp beads as the target lamp array; if the selected deviation region is the second optimization demand mark, set the sub-lamp array of the selected deviation region as the target lamp array; Step S402: generate a lamp array arrangement optimization instruction for the target lamp array, wherein the optimization instruction includes at least one parameter of adjusting the brightness or the irradiation direction of the lamp beads in the target lamp array; according to the optimization instruction, the lighting data of each lamp bead in the target lamp array is collected again to obtain an expected light intensity value of the target lamp array; if the difference between the expected light intensity value and the mean value of the lighting sub-regions of the same plant species is less than the expected deviation value, the lamp array arrangement scheme is confirmed and the adjustment is performed; otherwise, the target lamp array is reselected or the parameter is adjusted until the condition is met.

6. A light array optimization data analysis system for performing the light array optimization data analysis method for plant lighting according to any one of claims 1-5, characterized by: The analysis system includes a regional lighting collection module, a light intensity difference analysis module, a deviation demand analysis module, and a lamp array arrangement optimization module; The regional lighting analysis module is used to pre-deploy monitoring equipment in the plant growth region to collect lighting data in the plant growth region; the plant growth region is divided into several lighting sub-regions according to the plant distribution; The light intensity difference analysis module is used to evaluate the light intensity values of each lighting sub-region based on the collected lighting data; the light intensity values between different lighting sub-regions are compared and differences to identify several deviation regions that produce light intensity deviation; The deviation demand analysis module is used to capture deviation features of the lighting data in any deviation region; The expected adjustment state of each deviation feature is analyzed to identify the optimization demand of the deviation region; The lamp array arrangement optimization module is configured to generate an optimization demand instruction for each deviated area, extract a target lamp array meeting the optimization demand, and adjust the target lamp array of each deviated area according to a lamp array arrangement scheme.

7. The lamp array optimization data analysis system of claim 6, wherein: The area lighting analysis module comprises a lighting data acquisition unit and a lighting area division unit. The lighting data acquisition unit is configured to deploy a monitoring device in the plant growth area in advance, and acquire lighting data in the plant growth area.

8. The lamp array optimization data analysis system of claim 6, wherein: The area lighting analysis module comprises a lighting data acquisition unit and a lighting area division unit. The area light intensity analysis unit is configured to evaluate light intensity values of each lighting sub-area based on the acquired lighting data.

9. The lamp array optimization data analysis system of claim 6, wherein: The deviation feature capturing unit is configured to capture deviation features of the lighting data in each deviated area. The optimization demand identification unit is configured to analyze expected adjustment states of each deviation feature, and identify optimization demands of the deviated areas. The lamp array arrangement optimization module comprises a target lamp array extraction unit and a lamp array arrangement adjustment unit.

10. The lamp array optimization data analysis system of claim 6, wherein: The target lamp array extraction unit is configured to generate an optimization demand instruction for each deviated area, extract a target lamp array meeting the optimization demand, and the lamp array arrangement adjustment unit is configured to adjust the target lamp array of each deviated area according to a lamp array arrangement scheme. ​

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